package com.hao.chapter11;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.functions.ScalarFunction;

public class UdfTest_ScalarFunction {
    public static void main(String[] args) throws Exception{
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        //1.在创建表的DDL中直接定义时间属性
        String creatDDL = "CREATE TABLE clickTable (" +
                "user_name STRING," +
                "url STRING," +
                "ts BIGINT," +
                "et AS TO_TIMESTAMP( FROM_UNIXTIME(ts / 1000))," + //事件时间  FROM_UNIXTIME() 能转换为年月日时分秒这样的格式 转换秒
                " WATERMARK FOR et AS et - INTERVAL '1' SECOND " + //watermark 延迟一秒
                ")WITH(" +
                " 'connector' = 'filesystem'," +
                " 'path' = 'input/clicks.txt'," +
                " 'format' = 'csv'" +
                ")";

        tableEnv.executeSql(creatDDL);


        //2.注册自定义标量函数
        tableEnv.createTemporarySystemFunction("MyHash",MyHashFunction.class);

        //3.调用UDF进行查询转换 (查询当前user以及user的hashcode)
        Table resultTable = tableEnv.sqlQuery("select user_name,MyHash(user_name) from clickTable");

        //4.转换成流打印
        tableEnv.toDataStream(resultTable).print();

        env.execute();
    }

    //自定义实现ScalarFunction
    public static class MyHashFunction extends ScalarFunction{
        public int eval(String str){
            return str.hashCode();
        }
    }

}
